Adversarial reconstruction is inspired by dual learning [1]}, and it is adopted in deep da with the help of dual gan. Zhu et al. [2]} proposed Cycle gan, a technique for image-to-image translation without paired training examples. This algorithm employs two generators, which learn a mapping \(G: X \rightarrow Y\)  and the inverse mapping \(F: Y \rightarrow X\) . It exploits two discriminators \(D_X\)  and \(D_Y\)  and two losses; \(D_X\)  uses an adversarial loss to measure how realistic the generated image is (\(G(X) \approx Y\)  or \(G(Y) \approx X\) ); \(D_Y\)  utilizes a cycle consistency loss (or reconstruction loss) to measure how well the original input is reconstructed after a sequence of two generations (\(F(G(X)) \approx X\)  or \(G(F(Y)) \approx Y\) ). Similarly, dual gan [3]} and disco gan [4]} have been proposed. The former one, employed skip connections between mirrored downsampling and upsampling layers [5]}, [6]} for the generator, and the Markovian patch-gan architecture [7]} for the discriminator. The second one exploited as the reconstruction loss various distance functions such as mse, cosine distance and hinge loss.
